CN110584631A - Static human heartbeat and respiration signal extraction method based on FMCW radar - Google Patents

Static human heartbeat and respiration signal extraction method based on FMCW radar Download PDF

Info

Publication number
CN110584631A
CN110584631A CN201910957630.0A CN201910957630A CN110584631A CN 110584631 A CN110584631 A CN 110584631A CN 201910957630 A CN201910957630 A CN 201910957630A CN 110584631 A CN110584631 A CN 110584631A
Authority
CN
China
Prior art keywords
target
heartbeat
distance
human
signal
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201910957630.0A
Other languages
Chinese (zh)
Other versions
CN110584631B (en
Inventor
王勇
王文
田增山
周牧
何维
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Chongqing University of Post and Telecommunications
Original Assignee
Chongqing University of Post and Telecommunications
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Chongqing University of Post and Telecommunications filed Critical Chongqing University of Post and Telecommunications
Priority to CN201910957630.0A priority Critical patent/CN110584631B/en
Publication of CN110584631A publication Critical patent/CN110584631A/en
Application granted granted Critical
Publication of CN110584631B publication Critical patent/CN110584631B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/414—Discriminating targets with respect to background clutter
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction

Abstract

The invention provides a static human heartbeat and respiration signal extraction method based on an FMCW radar. Firstly, analyzing and calculating according to actual human body target detection data to obtain three parameter information of distance, Doppler and angle of the human body target. Then, three parameter images of a distance-time image, a distance-Doppler image and a distance-angle image are constructed. And then, detecting target individuals existing in the environment through the 2D-OS-CFAR by using the image, determining the human target to be detected, and simultaneously inhibiting the interference of the human target signal not to be detected on the human target signal to be detected. And finally, extracting heartbeat and respiratory signals of the detected human body target to be detected based on an extended DACM algorithm of derivative operation. The invention innovatively provides a simple and convenient extraction method of static human heartbeat and respiratory signals based on an FMCW radar, which realizes non-contact detection of the human heartbeat and respiratory signals and avoids constraint and discomfort of a patient caused by traditional contact detection equipment; meanwhile, the method can effectively inhibit interference and separate out a human target signal to be detected.

Description

Static human heartbeat and respiration signal extraction method based on FMCW radar
Technical Field
The invention relates to the field of vital sign signal detection, in particular to a static human heartbeat and respiration signal extraction method based on an FMCW radar.
Background
Respiration and heartbeat signals are an important index in modern medical detection, and the monitoring of the characteristic parameters of the heartbeat and the respiration signals provides reliable diagnosis and treatment basis for doctors. The traditional detection method is a contact detection technology, which is a technology used in the conventional heartbeat and respiratory signal monitoring equipment, and the purpose of monitoring heartbeat and respiratory signals is achieved mainly by directly contacting the body of a patient through a wearable sensor or a pasted electrode. Although the heartbeat and respiration signals monitored by this detection method have the advantages of high quality, low noise, etc., the method limits the patient's behavior and does not accurately reflect changes in the vital signs of the test subject. Meanwhile, the touch-based sensor is complex to operate in the using process, and the sensor may feel uncomfortable after being used for a long time.
Based on this, non-contact vital signs monitoring technology becomes the key to solve the above problems. A Frequency Modulated Continuous Wave (FMCW) non-contact life monitoring radar is a research hotspot in the technical field of non-contact life feature monitoring. The FMCW vital sign monitoring radar can monitor respiration and heartbeat signals for a long time in a long distance without contacting with an electrode or a sensor; compared with the traditional vital sign detection technology, the non-contact method makes the patient feel easier and more comfortable. However, due to the particularity of the vital sign signals, respiration and heartbeat signals are extremely weak and are easily covered by noise and clutter of the radar; therefore, in the prior art, the FMCW universal radar module is difficult to be directly used for heartbeat and respiration signal detection, and the manufacturing process and signal processing are complex, so that the FMCW radar has less application in the field of vital sign heartbeat and respiration signal detection.
Therefore, in order to solve the above problems in the prior art, it is necessary to provide a simple method for extracting static human heartbeat and respiratory signals based on FMCW radar, so as to solve the problems in the prior art.
Disclosure of Invention
Based on the defects and shortcomings of the existing vital sign signal extraction method, the invention provides a static human heartbeat and respiration signal extraction method based on FMCW. The method comprises the steps of firstly constructing a Range-Time-Map (RTM), a Range-Doppler-Map (RDM) and a Range-Angle-Map (RAM) image according to distance, Doppler and Angle information of a human target to detect the human target, and then extracting heartbeat and respiratory signals of the detected human target based on extended differential and cross multiplication (DACM) arc tangent demodulation. The method can effectively inhibit the interference of dynamic and static targets on the human target signals to be detected, separate the targets to be detected and extract the heartbeat and respiration signals of the human targets to be detected.
The technical scheme adopted by the invention is as follows: a static human heartbeat and respiration signal extraction method based on an FMCW radar mainly comprises two parts which are respectively used for human target detection and heartbeat and respiration signal extraction.
The method specifically comprises the following steps:
1) acquiring human body target information by using frequency modulation continuous waves to obtain radar intermediate frequency signals, and performing fast Fourier transform on single-frame intermediate frequency signals to obtain a distance vector matrix RM×1(ii) a Then multi-frame accumulation is carried out through time, and a distance-time matrix R is constructed by N frame distance vectors in a column formT＝[R1,R2,...RM]M×NSo as to obtain a Range-Time-Map (RTM), and determining a distance vector R with the strongest average power of distance units in the RTM Mapav,max。
2) Obtaining the distance vector R in the step oneav,maxFrame data, constructing a two-dimensional matrix RM×C(M is the number of sampling points, C is the number of sweep frequencies); to RM×CThe matrix array is subjected to distance dimension fast Fourier transform, and the row is subjected to speed dimension fast Fourier transform to obtain a distance-Doppler matrix RDPlotting distance-manyPoplerian diagram (Range-Doppler-Map, RDM).
3) And (3) performing two-dimensional unit ordered static false alarm rate (2D-OS-CFAR) target detection on the obtained RDM image, and then keeping a static detection target. The method comprises the following specific steps:
3a) applying a width of N on the RDM imagef×Nr4 × 7 two-dimensional reference sliding window, where NfWindow width, N, in the Doppler frequency dimensionrIs the window width in the distance dimension. Its valid reference unit W is 27.
3b) Sorting the sampling values of the training units in the two-dimensional reference window from small to large, and then taking the second stepEach sample value is used as an estimate of the total background clutter power level.
3c) From W, k, Pf(false alarm probability Pf＝e6) According to the formulaAnd calculating to obtain a self-adaptive normalized threshold value T, and then performing threshold judgment to obtain a detection target.
3d) The zero doppler detection target is retained in the range-doppler resolution unit.
4) And (3) constructing a vector matrix S by using frame data after the zero Doppler detection target is reserved in the third step, and then performing spectral peak search by using a Multiple Signal Classification (MUSIC) algorithm to obtain a Range-Angle-Map (RAM). And then selecting static target frame data with an angle of theta less than or equal to |5 degrees | in the RAM to obtain a human target signal B (t) to be detected.
5) Carrying out orthogonal down-conversion on a human body target signal B (t) to be detected to obtain two paths of signals B (I)/QI(t) and BQ(t) of (d). Collecting radar signals in an empty environment to obtain direct current bias dc of two paths of I/QIOAnd dcQOObtained by performing a DC correction operation using a differential amplifierThen, the two paths of signals I (t) and Q (t) are combined into a complex signal I (t) + j.Q (t).
6) Performing nonlinear arc tangent demodulation on the complex signal in the fifth stepProcessing and extracting the phase value of the heartbeat respiration signal in the target signal B (t) to be detectedThe arctangent trigonometric function calculation is then converted into a derivative operation using an extended differential cross multiplication (DACM) algorithmThe data is then stored, in discrete form, by time accumulation,is reduced to produce
7) Two fourth-order band-pass filters formed by second-order cascading are directly generated by using a Matlab fdantool tool, and the pass frequency bands of the four fourth-order band-pass filters are respectively [0.1Hz-0.5Hz ] and [0.8Hz-2.0Hz ]. And then, the phases of the extracted heartbeat and respiration phase signals are differentiated and then pass through a band-pass filter generated by fdatool, so that heartbeat and respiration signals are separated.
The invention has the following advantages: compared with the traditional technical method, the invention realizes non-contact detection of the vital sign signals of the human body by the FMCW radar, and avoids the constraint and discomfort brought to the patient by the traditional contact detection equipment. Based on the existing technical problem, a simple method for extracting heartbeat and respiration signals based on FMCW radar is provided. Meanwhile, the interference suppression method based on the 2D-OS-CFAR target detection effectively suppresses the interference of the interference target on the human body target to be detected and separates the human body target. Extended DACM phase extraction based on derivation operation avoids phase range limitation of the demodulation arctangent function, effectively solves the problem of phase ambiguity, and extracts heartbeat and respiratory signals.
Drawings
FIG. 1 block diagram of a heartbeat and respiration signal extraction system
FIG. 2 is a flow chart of human target examination
FIG. 3 is a flow chart of vital sign signal extraction
FIG. 4 distance-FFT graph
FIG. 5 RTM Signal diagram
FIG. 6 RDM Signal Chart
FIG. 7 RAM Signal diagrams
FIG. 82D-OS-CFAR detection flow diagram
FIG. 92 is a diagram showing the result of D-OS-CFAR detection
FIG. 10 RDM graph after interference suppression
FIG. 11 phase diagram before unwrapping
FIG. 12 DACM unwrapped phase diagram
FIG. 13 phase difference diagram
FIG. 14 respiratory wave diagrams
FIG. 15 is a waveform of heartbeat
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings:
the technical scheme adopted by the invention is as follows: a static human heartbeat and respiration signal extraction method based on an FMCW radar mainly comprises the following steps:
1) acquiring data, namely acquiring human body target information by using frequency modulation continuous waves to obtain radar intermediate frequency signals, estimating each frame of data parameters of the radar intermediate frequency signals to obtain information such as distance, angle, speed and the like, and determining human body detection targets after removing dynamic and static interference according to the distance, speed and angle information of the human body targets; and determining a human body detection target for the next step of vital feature extraction.
Antenna transmission signal:
wherein，Is a linear function of the frequency of the transmitted signal over time, fcIs the carrier frequency, B is the bandwidth, ATXIs the amplitude, T, of the transmitted signalcIs the sweep period, phi (t) is the phase noise.
Let R (t) be the displacement of thoracic cavity movement and the distance d from the radar sensor to the body0After a time delay tdObtaining a receiving signal:
mixing the echo signal with the transmitted signal, and passing through a low-pass filter to obtain an intermediate frequency signal sIF(t)：
Wherein the content of the first and second substances,it is the phase of the residual noise that,is the inherent phase shift.
2) Calculating a distance estimation value by using the intermediate frequency signal obtained in the step 1):
wherein f isIFRepresenting the frequency of the intermediate frequency signal, c the speed of light, B the bandwidth, TcIs a period.
FIG. 4 shows the result of distance-FFT of one frequency sweep of single frame data, which is obtained by averaging the number of C frequency sweeps of the frame to obtain the frame distance vector matrix RM×1Then, the N frame distance vectors are used for constructing a distance-time matrix R in a column formT＝[R1,R2,...RM]M×NThereby obtainingTo FIG. 5, and determining the distance vector R in the RTM chart with the strongest average power of the distance unitsav,maxThe picture is shown as bright in color, and the frame number of the frame is recorded.
3) Obtaining the distance vector R of the frame data shown by the frame number in the second stepav,maxBuilding a two-dimensional matrix RM×C(M is the number of sampling points, C is the number of sweep frequencies); to RM×CThe matrix array is subjected to distance dimension fast Fourier transform, and the row is subjected to speed dimension fast Fourier transform to obtain a distance-Doppler matrix RDA range-doppler plot is plotted, see figure 6.
4) Assuming a person sits still directly in front of the radar and a stationary person's breathing and chest displacement motion Δ x ≈ 12mm, the net velocity v produced is negligible and its frequency offset fdMuch smaller than the minimum doppler resolution of the FMCW radar of the present invention:
where c is the speed of light, f is the frequency point, λ is the wavelength, TcAnd N is the maximum sweep period number. Therefore, the obtained RDM image is firstly subjected to CFAR target detection in the doppler direction according to the flow shown in fig. 8, and the serial number of the target is recorded; final range-CFAR target detection is then performed on the recorded doppler sequence numbers. And after the two-dimensional unit order statistic constant false alarm 2D-OS-CFAR detection target is obtained, a static target is reserved, and a dynamic interference target with non-zero speed is restrained. The specific detection process comprises the following steps:
4a) applying a width of N on the RDM imagef×Nr4 × 7 two-dimensional reference sliding window, where NfWindow width, N, in the Doppler frequency dimensionrIs the window width in the distance dimension. Its valid reference unit W is 27.
4b) Sorting the sampling values of the training units in the two-dimensional reference window from small to large, and then taking the second stepTaking the sampled value as a totalAnd estimating the power level of the background clutter.
4c) From W, k, Pf(false alarm probability Pf＝e6) According to the formulaAnd calculating to obtain a self-adaptive normalized threshold value T, and then performing threshold judgment to obtain a detection target.
4d) The zero doppler detection target is retained in the range-doppler resolution unit.
5) Since a static interference target may exist in the environment, angle information needs to be extracted to remove the static interference target. The angle information is obtained by constructing a vector matrix and performing spectral peak search on the frame data by using a MUSIC algorithm.
Assume that the received signal model s (m, l) is:
construction of a vector matrix S from frame data
Performing spectrum peak search to obtain an RAM image, as shown in FIG. 7, removing static interference targets from different angles, and keeping static target frame data with theta less than or equal to |5 ° |; and obtaining a human target signal to be detected.
6) And extracting target heartbeat and respiratory signals from the determined vital sign detection target. And after the detected target is subjected to interference elimination, distance-FFT is carried out on the frame data, and a phase value is extracted from the identified distance interval to carry out vital sign estimation. In the formula (3) of step 1), for a single detection target, the signal form is:
7) to obtain the heartbeat and respiration signals, equation (8) needs to be subjected to nonlinear arc tangent demodulation. First of all, the first step is to,carrying out orthogonal down-conversion on a human body target signal to be detected to obtain two paths of I/Q signals BI(t) and BQ(t)。
In the formula AI/AQIs the amplitude, DC, of the two paths of I/QI/DCQRespectively, the direct current bias of two paths thereof. The dc bias is due to circuit imperfections such as spurious reflections and mixer self-mixing. Collecting radar signals in an empty environment to obtain two paths of direct current biases, and performing direct current correction operation by using a differential amplifier to obtain
After DC correction and DC offset removal, the two paths of I/Q signals are combined into a complex signal
In the formula ACIs the amplitude. Then the complex signal is demodulated to yield:
where θ is the intrinsic phase shift is constant and Δ φ is the residual noise phase is a constant term. In vital signs measurement, the relative displacement is mainly consideredConstant and constant terms are not considered. Therefore, equation (13) can be simplified as:
8) when using non-linear arctangent demodulation, it is noted that due to breathing and chest displacement motion, which is a multiple of the FMCW radar wavelength (4mm) in the present invention, is probably around 12 mm; the extracted phase value will exceed the phase range (-pi/2, pi/2) obtained by the arctan demodulation technique, which will bring about the phase ambiguity problem caused by phase discontinuity and phase jump (see fig. 11).
To solve this problem, an extended differential cross multiplication algorithm is used. Referring to fig. 12, the algorithm can automatically perform phase compensation and unwrapping to solve the problem of phase ambiguity. The DACM algorithm changes the arctangent function into a derivative operation, then
In the formula BI(t)' and BQ(t)' respectively of formula BIAnd BQIn differential form. The equation is expressed in discrete form and the integral is accumulated as:
9) the heartbeat spectrum range of the adult is generally between 0.1Hz and 0.5Hz, the respiratory spectrum range is between 0.8Hz and 2.0Hz, and the heartbeat spectrum range and the respiratory spectrum range are in different frequency intervals. Based on the four-order band-pass filter, two second-order band-pass filters are directly generated by using the fdatool command of Matlab, and the pass frequency band is the interval of heartbeat and respiration frequency spectrums. The extracted heartbeat and respiratory phase signals are then phase-differentiated and passed through a bandpass filter generated by fdatool to separate the respiratory and heartbeat signals, as shown in fig. 14 and 15.

Claims (4)

1. A static human heartbeat and respiration signal extraction method based on an FMCW radar is characterized by comprising the following steps:
1) acquiring human body target information by using Frequency Modulated Continuous Wave (FMCW) to obtain radar intermediate frequency signals, and performing fast Fourier transform on single-frame intermediate frequency signals to obtain a distance vector matrix RM×1(ii) a Then multi-frame accumulation is carried out through time, and a distance-time matrix R is constructed by N frame distance vectors in a column formT＝[R1,R2,...RM]M×NSo as to obtain a Range-Time-Map (RTM), and determining a distance vector R with the strongest average power of distance units in the RTM Mapav,max。
2) Obtaining the distance vector R in the step oneav,maxFrame data, constructing a two-dimensional matrix RM×C(M is the number of sampling points, C is the number of sweep frequencies); to RM×CThe matrix array is subjected to distance dimension fast Fourier transform, and the row is subjected to speed dimension fast Fourier transform to obtain a distance-Doppler matrix RDA Range-Doppler plot (RDM) is plotted.
3) And (3) performing two-dimensional unit ordered static false alarm rate (2D-OS-CFAR) target detection on the obtained RDM image, and then keeping a static detection target. The method comprises the following specific steps:
3a) applying a width of N on the RDM imagef×Nr4 × 7 two-dimensional reference sliding window, where NfWindow width, N, in the Doppler frequency dimensionrIs the window width in the distance dimension. Its valid reference unit W is 27.
3b) Sorting the sampling values of the training units in the two-dimensional reference window from small to large, and then taking the second stepEach sample value is used as an estimate of the total background clutter power level.
3c) From W, k, Pf(false alarm probability Pf＝e6) According to the formulaAnd calculating to obtain a self-adaptive normalized threshold value T, and then performing threshold judgment to obtain a detection target.
3d) The zero doppler detection target is retained in the range-doppler resolution unit.
4) And (3) constructing a vector matrix S by using frame data after the zero Doppler detection target is reserved in the third step, and then performing spectral peak search by using a Multiple Signal Classification (MUSIC) algorithm to obtain a Range-Angle-Map (RAM). And then selecting static target frame data with an angle of theta less than or equal to |5 degrees | in the RAM to obtain a human target signal B (t) to be detected.
5) Carrying out orthogonal down-conversion on a human body target signal B (t) to be detected to obtain two paths of signals B (I)/QI(t) and BQ(t) of (d). Collecting radar signals in an empty environment to obtain direct current bias dc of two paths of I/QIOAnd dcQOObtained by performing a DC correction operation using a differential amplifierThen, the two paths of signals I (t) and Q (t) are combined into a complex signal I (t) + j.Q (t).
6) Performing nonlinear arc tangent demodulation on the complex signal in the fifth stepProcessing and extracting the phase value of the heartbeat respiration signal in the target signal B (t) to be detectedThe arctangent trigonometric function calculation is then converted into a derivative operation using an extended differential cross multiplication (DACM) algorithmThe data is then stored, in discrete form, by time accumulation,is reduced to produce
7) Two fourth-order band-pass filters formed by second-order cascading are directly generated by using a Matlab fdantool method, and the pass frequency bands of the four fourth-order band-pass filters are respectively [0.1Hz-0.5Hz ] and [0.8Hz-2.0Hz ]. And then, the phases of the extracted heartbeat and respiration phase signals are differentiated and then pass through a band-pass filter generated by fdatool, so that heartbeat and respiration signals are separated.
2. The method for extracting static human heartbeat and respiratory signals based on FMCW radar as claimed in claim 1, wherein a simple and convenient process for extracting static human heartbeat and respiratory signals based on FMCW radar is provided.
3. The method for extracting static human heartbeat and respiratory signals based on FMCW radar as claimed in claim 1, wherein the interference suppression method based on 2D-OS-CFAR target detection in steps 3) and 4).
4. The FMCW radar-based static human heartbeat and respiration signal vital sign signal extraction method as claimed in claim 1, wherein the extended DACM phase extraction based on derivation in steps 5) and 6).
CN201910957630.0A 2019-10-10 2019-10-10 Static human heartbeat and respiration signal extraction method based on FMCW radar Active CN110584631B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910957630.0A CN110584631B (en) 2019-10-10 2019-10-10 Static human heartbeat and respiration signal extraction method based on FMCW radar

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910957630.0A CN110584631B (en) 2019-10-10 2019-10-10 Static human heartbeat and respiration signal extraction method based on FMCW radar

Publications (2)

Publication Number Publication Date
CN110584631A true CN110584631A (en) 2019-12-20
CN110584631B CN110584631B (en) 2022-03-25

Family

ID=68866048

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910957630.0A Active CN110584631B (en) 2019-10-10 2019-10-10 Static human heartbeat and respiration signal extraction method based on FMCW radar

Country Status (1)

Country Link
CN (1) CN110584631B (en)

Cited By (30)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111091831A (en) * 2020-01-08 2020-05-01 上海交通大学 Silent lip language recognition method and system
CN111160376A (en) * 2019-12-31 2020-05-15 联想(北京)有限公司 Information processing method and device, electronic equipment and storage medium
CN111505601A (en) * 2020-05-21 2020-08-07 上海交通大学 Linear motion demodulation implementation method based on improved differential cross multiplication
CN111856452A (en) * 2020-05-21 2020-10-30 重庆邮电大学 OMP-based static human heartbeat and respiration signal separation and reconstruction method
CN111983595A (en) * 2020-07-14 2020-11-24 北京航空航天大学杭州创新研究院 Indoor positioning method and device
CN111990968A (en) * 2020-08-07 2020-11-27 北京航空航天大学杭州创新研究院 Signal monitoring method, device, storage medium and system
CN112098996A (en) * 2020-08-21 2020-12-18 广东工业大学 Anti-interference vital sign information extraction and separation method based on millimeter wave biological radar
CN112137604A (en) * 2020-10-22 2020-12-29 温州大学 Respiration and heartbeat detection method and system based on continuous wave Doppler radar
CN112617748A (en) * 2020-11-02 2021-04-09 佛山科学技术学院 Method and system for processing sign data based on frequency modulation continuous wave radar
CN112741611A (en) * 2020-12-25 2021-05-04 上海交通大学 Multi-human body vital sign synchronous monitoring system and method based on millimeter wave sensing
CN112965060A (en) * 2021-02-19 2021-06-15 加特兰微电子科技(上海)有限公司 Detection method and device for vital sign parameters and method for detecting physical sign points
CN113261925A (en) * 2021-03-11 2021-08-17 山东师范大学 Human body sensing method and system based on millimeter wave radar
WO2021159294A1 (en) * 2020-02-12 2021-08-19 Oppo广东移动通信有限公司 Physical sign detection method, apparatus, and device
CN113397524A (en) * 2021-06-16 2021-09-17 深圳大学 Respiration detection method, device, equipment and storage medium
CN113406610A (en) * 2021-06-16 2021-09-17 深圳大学 Target detection method, device, equipment and storage medium
CN113440120A (en) * 2021-06-17 2021-09-28 北京航空航天大学 Millimeter wave radar-based method for detecting respiration and heartbeat of person
CN113545764A (en) * 2021-07-16 2021-10-26 中国人民解放军国防科技大学 FMCW radar personnel identity verification data processing method and system
CN113567947A (en) * 2021-07-21 2021-10-29 天津职业技术师范大学(中国职业培训指导教师进修中心) Demodulation method of Doppler phase shift signal
CN113633268A (en) * 2020-05-11 2021-11-12 富士通株式会社 Physiological signal detection method and device and data processing equipment
CN113687351A (en) * 2021-09-14 2021-11-23 无锡威孚高科技集团股份有限公司 Security radar monitoring and early warning system capable of reducing false alarm rate
CN113729655A (en) * 2021-09-26 2021-12-03 重庆邮电大学 Method for separating received signals of UWB radar sensor
CN113796845A (en) * 2021-06-10 2021-12-17 重庆邮电大学 Image processing-based driver heart rate identification method
CN114159048A (en) * 2021-12-06 2022-03-11 河南牧原智能科技有限公司 Method for detecting respiratory rate of swinery and related product
CN114246563A (en) * 2021-12-17 2022-03-29 重庆大学 Intelligent heart and lung function monitoring equipment based on millimeter wave radar
CN114403848A (en) * 2022-01-05 2022-04-29 河南牧原智能科技有限公司 Method, system and storage medium for detecting respiratory frequency of animal population
CN114469025A (en) * 2021-12-28 2022-05-13 中国地质大学（武汉） Multi-target respiration and heartbeat signal detection method for FMCW radar
WO2022231525A1 (en) * 2021-04-29 2022-11-03 新加坡国立大学 Vital sign detection apparatus and system and data processing method
CN115308734A (en) * 2019-12-26 2022-11-08 华为技术有限公司 Respiratory data calculation method and related equipment
CN117148308A (en) * 2023-10-30 2023-12-01 南京航空航天大学 Method and system for detecting shielding of vital sign radar in cabin
CN112965058B (en) * 2021-02-01 2024-02-02 西安电子科技大学 Identity verification method, device and equipment based on breathing characteristics and computer storage medium

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2517621A1 (en) * 2011-04-29 2012-10-31 Nederlandse Organisatie voor toegepast -natuurwetenschappelijk onderzoek TNO A radar apparatus for detecting multiple life-signs of a subject, a method and a computer program product
CN106125053A (en) * 2016-06-17 2016-11-16 中国电子科技集团公司第十研究所 Pulse Doppler radar polarization anti jamming method
CN106175723A (en) * 2016-06-27 2016-12-07 中国人民解放军第三军医大学第附属医院 A kind of many life monitoring systems based on FMCW wideband radar
CN106291524A (en) * 2016-08-05 2017-01-04 南京理工大学 LFMCW radar detection movement human mesh calibration method based on anthropometric dummy
CN106821347A (en) * 2016-12-20 2017-06-13 中国人民解放军第三军医大学 A kind of life detection radar breathing of FMCW broadbands and heartbeat signal extraction algorithm
CN108072864A (en) * 2016-11-07 2018-05-25 中国科学院沈阳自动化研究所 A kind of multiple target detection method based on change carrier frequency tune frequency sequence
CN108919247A (en) * 2018-03-30 2018-11-30 中国科学院电子学研究所 A kind of multiple target physical examination survey and localization method based on constant false alarm rate detection
CN109522826A (en) * 2018-10-31 2019-03-26 广东工业大学 A kind of life signal detection method and system based on FMCW millimetre-wave radar
CN109965858A (en) * 2019-03-28 2019-07-05 北京邮电大学 Based on ULTRA-WIDEBAND RADAR human body vital sign detection method and device
CN110109078A (en) * 2019-05-05 2019-08-09 西安电子科技大学 Velocity solution blur method based on coherent frequency agile radar
CN110187342A (en) * 2019-05-14 2019-08-30 南京理工大学 A kind of vital sign detection and imaging method based on FMCW mobile platform

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2517621A1 (en) * 2011-04-29 2012-10-31 Nederlandse Organisatie voor toegepast -natuurwetenschappelijk onderzoek TNO A radar apparatus for detecting multiple life-signs of a subject, a method and a computer program product
CN106125053A (en) * 2016-06-17 2016-11-16 中国电子科技集团公司第十研究所 Pulse Doppler radar polarization anti jamming method
CN106175723A (en) * 2016-06-27 2016-12-07 中国人民解放军第三军医大学第附属医院 A kind of many life monitoring systems based on FMCW wideband radar
CN106291524A (en) * 2016-08-05 2017-01-04 南京理工大学 LFMCW radar detection movement human mesh calibration method based on anthropometric dummy
CN108072864A (en) * 2016-11-07 2018-05-25 中国科学院沈阳自动化研究所 A kind of multiple target detection method based on change carrier frequency tune frequency sequence
CN106821347A (en) * 2016-12-20 2017-06-13 中国人民解放军第三军医大学 A kind of life detection radar breathing of FMCW broadbands and heartbeat signal extraction algorithm
CN108919247A (en) * 2018-03-30 2018-11-30 中国科学院电子学研究所 A kind of multiple target physical examination survey and localization method based on constant false alarm rate detection
CN109522826A (en) * 2018-10-31 2019-03-26 广东工业大学 A kind of life signal detection method and system based on FMCW millimetre-wave radar
CN109965858A (en) * 2019-03-28 2019-07-05 北京邮电大学 Based on ULTRA-WIDEBAND RADAR human body vital sign detection method and device
CN110109078A (en) * 2019-05-05 2019-08-09 西安电子科技大学 Velocity solution blur method based on coherent frequency agile radar
CN110187342A (en) * 2019-05-14 2019-08-30 南京理工大学 A kind of vital sign detection and imaging method based on FMCW mobile platform

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
OCTAVIAN POSTOLACHE等: "Microwave FMCW Doppler radar implementation for", 《2010 IEEE INTERNATIONAL WORKSHOP ON MEDICAL MEASUREMENT AND APPLICATIONS》 *

Cited By (43)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115308734A (en) * 2019-12-26 2022-11-08 华为技术有限公司 Respiratory data calculation method and related equipment
CN111160376A (en) * 2019-12-31 2020-05-15 联想(北京)有限公司 Information processing method and device, electronic equipment and storage medium
CN111160376B (en) * 2019-12-31 2023-11-24 联想(北京)有限公司 Information processing method, device, electronic equipment and storage medium
CN111091831B (en) * 2020-01-08 2023-04-07 上海交通大学 Silent lip language recognition method and system
CN111091831A (en) * 2020-01-08 2020-05-01 上海交通大学 Silent lip language recognition method and system
WO2021159294A1 (en) * 2020-02-12 2021-08-19 Oppo广东移动通信有限公司 Physical sign detection method, apparatus, and device
CN113633268A (en) * 2020-05-11 2021-11-12 富士通株式会社 Physiological signal detection method and device and data processing equipment
CN111505601A (en) * 2020-05-21 2020-08-07 上海交通大学 Linear motion demodulation implementation method based on improved differential cross multiplication
CN111856452A (en) * 2020-05-21 2020-10-30 重庆邮电大学 OMP-based static human heartbeat and respiration signal separation and reconstruction method
CN111856452B (en) * 2020-05-21 2022-09-20 重庆邮电大学 OMP-based static human heartbeat and respiration signal separation and reconstruction method
CN111983595A (en) * 2020-07-14 2020-11-24 北京航空航天大学杭州创新研究院 Indoor positioning method and device
CN111983595B (en) * 2020-07-14 2023-11-10 北京航空航天大学杭州创新研究院 Indoor positioning method and device
CN111990968A (en) * 2020-08-07 2020-11-27 北京航空航天大学杭州创新研究院 Signal monitoring method, device, storage medium and system
CN112098996B (en) * 2020-08-21 2023-07-07 广东工业大学 Anti-interference extraction and separation vital sign information method based on millimeter wave biological radar
CN112098996A (en) * 2020-08-21 2020-12-18 广东工业大学 Anti-interference vital sign information extraction and separation method based on millimeter wave biological radar
CN112137604A (en) * 2020-10-22 2020-12-29 温州大学 Respiration and heartbeat detection method and system based on continuous wave Doppler radar
CN112617748A (en) * 2020-11-02 2021-04-09 佛山科学技术学院 Method and system for processing sign data based on frequency modulation continuous wave radar
CN112617748B (en) * 2020-11-02 2022-04-26 佛山科学技术学院 Method and system for processing sign data based on frequency modulation continuous wave radar
CN112741611A (en) * 2020-12-25 2021-05-04 上海交通大学 Multi-human body vital sign synchronous monitoring system and method based on millimeter wave sensing
CN112965058B (en) * 2021-02-01 2024-02-02 西安电子科技大学 Identity verification method, device and equipment based on breathing characteristics and computer storage medium
CN112965060A (en) * 2021-02-19 2021-06-15 加特兰微电子科技(上海)有限公司 Detection method and device for vital sign parameters and method for detecting physical sign points
CN113261925A (en) * 2021-03-11 2021-08-17 山东师范大学 Human body sensing method and system based on millimeter wave radar
WO2022231525A1 (en) * 2021-04-29 2022-11-03 新加坡国立大学 Vital sign detection apparatus and system and data processing method
CN113796845B (en) * 2021-06-10 2023-08-04 重庆邮电大学 Image processing-based driver heart rate recognition method
CN113796845A (en) * 2021-06-10 2021-12-17 重庆邮电大学 Image processing-based driver heart rate identification method
CN113397524A (en) * 2021-06-16 2021-09-17 深圳大学 Respiration detection method, device, equipment and storage medium
CN113406610A (en) * 2021-06-16 2021-09-17 深圳大学 Target detection method, device, equipment and storage medium
CN113406610B (en) * 2021-06-16 2023-06-23 深圳大学 Target detection method, device, equipment and storage medium
CN113440120A (en) * 2021-06-17 2021-09-28 北京航空航天大学 Millimeter wave radar-based method for detecting respiration and heartbeat of person
CN113545764A (en) * 2021-07-16 2021-10-26 中国人民解放军国防科技大学 FMCW radar personnel identity verification data processing method and system
CN113545764B (en) * 2021-07-16 2022-03-25 中国人民解放军国防科技大学 FMCW radar personnel identity verification data processing method and system
CN113567947A (en) * 2021-07-21 2021-10-29 天津职业技术师范大学(中国职业培训指导教师进修中心) Demodulation method of Doppler phase shift signal
CN113567947B (en) * 2021-07-21 2023-07-28 天津职业技术师范大学(中国职业培训指导教师进修中心) Demodulation method of Doppler phase shift signal
CN113687351A (en) * 2021-09-14 2021-11-23 无锡威孚高科技集团股份有限公司 Security radar monitoring and early warning system capable of reducing false alarm rate
CN113729655A (en) * 2021-09-26 2021-12-03 重庆邮电大学 Method for separating received signals of UWB radar sensor
CN113729655B (en) * 2021-09-26 2024-03-08 重庆邮电大学 Method for separating UWB radar sensor receiving signals
CN114159048A (en) * 2021-12-06 2022-03-11 河南牧原智能科技有限公司 Method for detecting respiratory rate of swinery and related product
CN114246563A (en) * 2021-12-17 2022-03-29 重庆大学 Intelligent heart and lung function monitoring equipment based on millimeter wave radar
CN114246563B (en) * 2021-12-17 2023-11-17 重庆大学 Heart and lung function intelligent monitoring equipment based on millimeter wave radar
CN114469025A (en) * 2021-12-28 2022-05-13 中国地质大学（武汉） Multi-target respiration and heartbeat signal detection method for FMCW radar
CN114403848A (en) * 2022-01-05 2022-04-29 河南牧原智能科技有限公司 Method, system and storage medium for detecting respiratory frequency of animal population
CN117148308B (en) * 2023-10-30 2024-02-02 南京航空航天大学 Method and system for detecting shielding of vital sign radar in cabin
CN117148308A (en) * 2023-10-30 2023-12-01 南京航空航天大学 Method and system for detecting shielding of vital sign radar in cabin

Also Published As

Publication number Publication date
CN110584631B (en) 2022-03-25

Similar Documents

Publication Publication Date Title
CN110584631B (en) Static human heartbeat and respiration signal extraction method based on FMCW radar
CN109965858B (en) Ultra-wideband radar-based human body vital sign detection method and device
CN109875529B (en) Vital sign detection method and system based on ultra-wideband radar
WO2022104868A1 (en) Millimeter wave radar-based non-contact real-time vital sign monitoring system and method
CN107607923B (en) LFMCW radar-based vibration monitoring system and signal processing method
CN111568399A (en) Radar-based respiration and heartbeat signal detection method and system
CN112336322B (en) Contactless respiration or heartbeat detection method
CN113854981A (en) Millimeter wave radar-based sports multi-person vital sign monitoring method
CN115399747B (en) Multi-person vital sign monitoring method based on millimeter wave radar
CN115644840A (en) Vital sign detection method based on millimeter wave radar
CN113009584B (en) Ultra-wideband MIMO radar vital sign detection positioning method
CN116077044A (en) Vital sign detection method based on millimeter wave radar
CN113951856A (en) Frequency spectrum estimation method based on Doppler radar heartbeat detection
CN114403820A (en) Vital sign detection method and system for moving target
CN111175724B (en) Target micro Doppler component extraction method based on centroid calculation
Salman et al. Contactless vital signs tracking with mmWave RADAR in realtime
WO2023158033A1 (en) Contactless biosignal measurement system and method
Zhao et al. Multi-target vital signs remote monitoring using mmWave FMCW radar
CN115736872A (en) Method for calculating heart rate in real time based on millimeter wave radar
CN115969351A (en) Method for calculating respiration in real time based on millimeter wave radar
CN115736886A (en) Multi-target respiration rate parameter estimation method based on millimeter wave radar
CN116087935A (en) Three-dimensional positioning and identity recognition method and system based on millimeter wave radar
CN112617748B (en) Method and system for processing sign data based on frequency modulation continuous wave radar
CN115153459A (en) Top-mounted millimeter wave radar heartbeat and respiration detection device and detection method
Chen et al. A Novel Scheme for Suppression of Human Motion Effects in Non-Contact Heart Rate Detection

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant